3 dimension sound source localization with cross-correlation and CORDIC algorithm on FPGA

2016 ◽  
Author(s):  
Agung Nuza Dwiputra ◽  
Riko Hasiando Goknipasu Nainggolan ◽  
Muhammad Arief Ma'ruf Nasution
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Cross Correlation ◽  
Sound Source Localization ◽  
Cordic Algorithm

scholarly journals A modified cross-correlation algorithm to achieve the time difference of arrival in sound source localization

2019 ◽  
Vol 52 (3-4) ◽  
pp. 212-221 ◽  
Author(s):  
Na Zhu ◽  
Tamim Reza
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Time Difference ◽  
Sound Source Localization ◽  
Time Data ◽  
Time Difference Of Arrival ◽  
Correlation Algorithm ◽  
Target Source

The accuracy in ranging and direction in locating a target source is crucial in sound source localization and different methods have been proposed due to important applications of sound source localization. One of the methods in sound source localization is triangulation with the time difference of arrival information. In this literature, a modified cross-correlation algorithm is introduced to increase the accuracy in time difference of arrival, thus further improving the sound source localization results. A numerical model is generated by assuming multiple sound sources broadcasting in room environment and the location of the target sound source is identified with the triangulation algorithm. Real-time data are produced through experimental setup using an array of four microphones with a target source and background noise. The signals are processed by modified cross-correlation and conventional cross-correlation for comparison. The impacts of the signal-to-noise ratio and time difference of arrival on sound source localization results are demonstrated and discussed. Experimental validation conducted in a non-ideal environment has shown that the modified cross-correlation algorithm can minimize the error in time difference of arrival to be used in sound source localization, thus improving the accuracy in both sound source ranging and direction.

Research on Sound Source Localization System Based on Time Delay Estimation

2013 ◽  
Vol 397-400 ◽  
pp. 2209-2214
Author(s):  
Chuan Yi Zhang ◽  
Chang Wei Mi ◽  
Pei Yang Yao
Keyword(s):  
Time Delay ◽  
Source Localization ◽  
Sound Source ◽  
Cross Correlation ◽  
Microphone Array ◽  
Estimation Method ◽  
Correlation Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Sound Source Localization

In the estimation of time delay, there always would not appear obvious peak with the basic cross-correlation (CC). In order to solve the problem of the basic cross-correlation method, this essay represents an improved time delay estimation method based on the generalized cross-correlation (GCC) and combines with the microphone array structure to achieve sound source localization. Finally, the simulation results show that this method could measure the sound source’s location accurately with noise and reverberation, and the distance positioning error is less than 10cm, the direction angle error is below 3°.

scholarly journals Active Binaural Auditory Perceptual System for a Socially Interactive Humanoid Robot

2022 ◽  
Vol 12 (1) ◽  
pp. 83
Author(s):  
Sohaib Siddique Butt ◽  
Mahnoor Fatima ◽  
Ali Asghar ◽  
Wasif Muhammad
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Humanoid Robot ◽  
Cross Correlation ◽  
Perceptual System ◽  
Sound Source Localization ◽  
Signal Processing Technique ◽  
Gaze Shift ◽  
Perception System

Sound Source Localization (SSL) and gaze shift to the sound source behavior is an integral part of a socially interactive humanoid robot perception system. In noisy and reverberant environments, it is non-trivial to estimate the location of a sound source and accurately shift gaze in its direction. Previous SSL algorithms are deficient in the optimum approximation of distance to audio sources and to accurately detect, interpret, and differentiate the actual sound from comparable sound sources due to challenging acoustic environments. In this article, a learning-based model is presented to achieve noiseless and reverberation-resistant sound source localization in the real-world scenarios. The proposed system utilizes a multi-layered Gaussian Cross-Correlation with Phase Transform (GCC-PHAT) signal processing technique as a baseline for a Generalized Cross Correlation Convolution Neural Network (GCC-CNN) model. The proposed model is integrated with an efficient rotation algorithm to predict and orient toward the sound source. The performance of the proposed method is compared with the state-of-art deep network-based sound source localization methods. The findings of the proposed method outperform the existing neural network-based approaches by achieving the highest accuracy of 96.21% for an active binaural auditory perceptual system.

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